Itamar Arel's talk focused on AGI (Artificial General Intelligence), where he described a two pronged path toward achieving AGI that might take us years instead of decades. He believes that we already have the pieces of the puzzle to build human level AI system, that it's really a question of creating a properly-focused engineering effort.
He then went on to explain the two sides of his parallel approach to achieving AGI. The first part is what he called "Deep Machine Learning," a biologically-inspired framework for learning about the world with which we interact. A deep machine learning system builds a hierarchical model of the world in order to infer things about that world, and predict future events that might occur. It can discover structure based on spatial and temporal regularities in sensory observations, and deliver a powerful situation inference engine.
The complementary part is the decision making aspect of such a system, which he argues is driven by rewards. In particular, he feels reinforcement learning is at the core of intelligent decision making, which combined with deep machine learning can yield a breakthrough in AGI. Given the great advancements in large-scale integrated electronics, he claims that an AGI system which based on these principles may be built in the very near future.
Do you think we already have the building blocks to create AGI in the next ten or twenty years? Tell us about your thoughts right here on the blog.
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